Light Source and Trail Recognition via Optical Spectrum Feature Analysis for Optical Network Security

Abstract

In this letter, we propose a light source and trail recognition method via machine-learning-based optical spectrum feature analysis (OSFA) for optical network security. In order to realize light source and light trail recognition, we proposed a t-distribution stochastic neighbor embedding and support vector machine based OSFA method for stand-alone system. In order to improve the recognition accuracy, we further proposed a derivative cooperative multi-node recognition (CoMNR) mechanism. Experimental results show that 100% and 98.5% recognition accuracy for light sources and light trails can be achieved respectively at a single node. When CoMNR is adopted, the light trail recognition accuracy can be further improved to 100%.